Student neural network
First Claim
1. A neural network system comprising:
- a teacher neural network, said teacher neural network capable of receiving and processing a problem set so as to produce a series of tutoring inputs, said tutoring inputs including an output solution to said problem set; and
a student neural network trained using said tutoring inputs to produce a student output that closely approximates said output solution, and is operable to be retrained by repeatedly processing a different set of tutoring inputs derived from a different teacher network.
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Abstract
A student neural network that is capable of receiving a series of tutoring inputs from one or more teacher networks to generate a student network output that is similar to the output of the one or more teacher networks. The tutoring inputs are repeatedly processed by the student until, using a suitable method such as back propagation of errors, the outputs of the student approximate the outputs of the teachers within a predefined range. Once the desired outputs are obtained, the weights of the student network are set. Using this weight set the student is now capable of solving all of the problems of the teacher networks without the need for adjustment of its internal weights. If the user desires to use the student to solve a different series of problems, the user only needs to retrain the student by supplying a different series of tutoring inputs.
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Citations
18 Claims
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1. A neural network system comprising:
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a teacher neural network, said teacher neural network capable of receiving and processing a problem set so as to produce a series of tutoring inputs, said tutoring inputs including an output solution to said problem set; and a student neural network trained using said tutoring inputs to produce a student output that closely approximates said output solution, and is operable to be retrained by repeatedly processing a different set of tutoring inputs derived from a different teacher network. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method of using a student neural network to produce student outputs that closely approximate an output solution produced by a teacher neural network comprising:
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providing a teacher network with teacher weights set to produce an output solution in response to a specific problem set input; transferring said teacher weights, said problem set input, and said output solution to said student network having student weights; training said student network using said teacher weights, said problem set input, and said output solution so as to arrive at a setting for said student weights that causes said student network to produce a student output that closely approximates said output; and retraining said student network through the introduction of a second set of teacher weights, a second problem set input, and a second output solution all produced by a second teacher network. - View Dependent Claims (12, 13, 14, 15)
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16. A neural network system comprising:
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a first teacher neural network having first internal weights operable to solve a first problem set; and a student neural network in receipt of said first internal weights, said student neural network operable to solve said first problem set using said first internal weights. - View Dependent Claims (17, 18)
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Specification